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Hydraulic fracture visualization by processing ultrasonic transmission waveforms using unsupervised learning Α
  • Aditya Chakravarty,
  • Siddharth Misra,
  • Chandra Shekhar Rai
Aditya Chakravarty
Texas A&M University, Texas A&M University
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Siddharth Misra
Texas A&M University, Texas A&M University

Corresponding Author:misra@tamu.edu

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Chandra Shekhar Rai
University of Oklahoma, University of Oklahoma
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Ultrasonic transmission is sensitive to the variation in mechanical properties of materials. Wave propagation through fractured media introduces changes in the frequency content, travel time and transmission coefficient of the wave. A workflow based on physics-driven unsupervised learning is developed to process the transmitted ultrasonic-shear waveforms to non-invasively visualize the geomechanical alterations due to hydraulic fracturing of a tight sandstone. Novelty of the work involves the assignment of physically consistent clusters to the measurements of shear waveforms across the axial and frontal planes by incorporating the travel time of the peak of spectral energy and transmission coefficient. The proposed workflow generates maps of geomechanical alterations across the frontal and axial planes of the sample. The outputs of the workflow are in good agreement with independent techniques viz. acoustic emission and X-ray computed tomography. The proposed workflow can be adapted for improved fracture characterization in the subsurface when processing sonic-logging, cross-wellbore seismic or surface seismic waveform data.